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在进行粒子群优化的收敛性理论分析的基础上,推出了保证粒子群优化算法收敛性的参数设置区域,合理选择粒子群算法的关键参数,将粒子群优化与广义预测控制有机融合,用粒子群算法来解决广义预测控制的优化问题,提出基于粒子群优化的广义预测控制算法,通过工业过程对象的仿真并和传统的广义预测控制算法进行了对比分析,表明了该算法的有效性,特别是算法具有良好的输出跟踪精度和较强的鲁棒性.
Based on the theoretical analysis of convergence of particle swarm optimization, a parameter setting area is proposed to ensure the convergence of particle swarm optimization algorithm. The key parameters of particle swarm optimization are reasonably selected. Particle swarm optimization and generalized predictive control are organically integrated. Group algorithm to solve the problem of generalized predictive control optimization. A generalized predictive control algorithm based on particle swarm optimization is proposed. Through the simulation of industrial process objects and compared with the traditional generalized predictive control algorithm, the effectiveness of the algorithm is demonstrated. In particular, The algorithm has good output tracking accuracy and strong robustness.